To produce modern microdevices such as integrated circuits with photolithographic techniques, most photolithographic reticles or masks employ some sort of resolution enhancement technology (RET). Examples of RETs include optical and process correction (OPC, sometimes also called optical proximity correction), phase shifters, subresolution assist features, off-axis illumination and other techniques that, in effect, allow for precompensation of distortions that occur in a lithographic patterning system in order to improve the ability of the system to print a desired pattern of objects on a semiconductor wafer.
To apply these RETs, the effect of these distortions on the actual geometric structures of a microdevice must be predicted. This prediction is usually done using simulation tools that correspond to the various aspects of the imaging and pattern process, including the lithographic imaging, the development and baking of the photoresist, and etching or deposition to form the final device structures. For the application of conventional RETs to semiconductor integrated circuits, the data for each critical layer of an IC is examined using various simulators, and the impact of the distortions assessed. When the resulting features are predicted to be outside of predetermined tolerance ranges, the data defining the layer is altered to compensate for the distortions.
These alterations or corrections are typically carried out at the time the device design undergoes final physical verification. As shown in FIG. 1, a typical design verification and RET process includes receiving a data layout file 45 or portion thereof, that defines a desired pattern of objects to be created on a wafer. This is typically a layer of a device layout in a standard format such as GDS-II, although other formats can also be used. Ultimately, the data in these layers will be used to define the shape of the openings in the photolithographic reticle or mask that will be used in a photolithography system. Consequently, a simulation of the intensity of the projected light from a photomask fabricated from this data at any given point on the wafer is carried out, using an image intensity model 50. From the results of the simulated image intensity, OPC or other RETs 52 are applied to the layout data to compensate for the predicted distortions and improve the resolution and pattern fidelity of the printed objects. A corrected layout file 54, including the applied RETs, is provided to a mask writing tool 56 that produces a number of masks or reticles 58 (hereinafter commonly referred to as a mask) used in the lithographic system to produce the desired devices on wafers.
In a conventional image intensity model, the light passing through various portions of the mask is modeled as a binary process with 100% light transmission occurring in transparent areas 60 on the mask and 0% transmission occurring in opaque areas 62 of the mask. Alternatively, if other types of masks are used such as alternating and attenuating phase-shifting instead of chrome-on-glass (COG), a simplistic model is generally assumed where the mask model is still “binary” but the transmission and phase of the various mask areas receive appropriate values (6% transmission with 180 degree phase for attenuating PSM and 100% transmission with 180 degree phase for alternating PSM).
In fact, phase-shifting masks can have fairly complicated 3-dimensional structures, and are far from “binary”. Common phase-shifting structures are created by creating topographic structures in the surface of the mask. These are illustrated in FIG. 2A. The difference in refractive index between the glass mask substrate and air provides a phase shift when apertures of different topography are used. When this phase-shift is 180 degrees, destructive interference occurs between light passing through the two apertures, and the resulting dark interference fringes in the image on the wafer can have highly desirable contrast and depth-of-focus properties.
The topographic patterns on the mask, however, can also have unintended properties. For the phase shifting structure shown in FIG. 2A, the cross section of the mask shows two apertures side by side, one phase shifted and one not. In this case, the phase shift is created by etching into the substrate, although other techniques for mask fabrication by selective deposition are also possible. When the two apertures are of the same width, a “binary” model would predict that the corresponding images should also be identical. However, in fact, additional scattering of light occurs from the edges of the etched apertures. This ultimately reduces the intensity of the light in the image formed from the phase shifted aperture, as shown in FIG. 2B.
Various techniques can be used to compensate for these effects. One is to use a more complicated etch procedure, in which an “undercut” behind the opaque material is formed. This is illustrated in FIG. 2C. This can reduce the imbalance between the two intensities, but it is not eliminated. The mask is still far from being adequately described by a simple “binary” description. The results are exacerbated when these masks with topography are used with off-axis illumination, where the topographic structures may have more dramatic scattering properties than when used at normal incidence.
It is known that applying the simplistic “binary” model of the mask transmission will not accurately describe the images of the mask, and therefore produces errors in the application of RETs to the mask layout. While more sophisticated mask models for computing accurate 3-dimensional electromagnetic fields at photomasks are known, such as the product TEMPEST developed at UC Berkeley and now offered for sale by Panoramic Technologies, they have not been implemented in software for the verification and RET processing of full chip integrated device designs because the models are computationally intensive. Using such a solver for all the millions of feature edges in a typical IC layout would take an impractically long amount of time—days or even weeks. Results are desired in minutes or hours at the longest. Given these problems, there is a need for a system for improving the accuracy of image intensity calculations without significantly increasing processing time.